This paper is concerned with the parameter estimation of a
relatively general class of nonlinear dynamic systems. A Maximum
Likelihood (ML) framework is employed in the interests of
statistical efficiency, and it is illustrated how an Expectation
Maximisation (EM) algorithm may be used to compute these ML
estimates. An essential ingredient is the employment of so-called
``particle smoothing'' methods to compute required conditional
expectations via a Monte Carlo approach. A simulation example
demonstrates the efficacy of these techniques.